Automatic speech recognition (ASR) systems have found widespread usage in a host of different and varied applications. Some example applications include, but are not limited to, telephony, data entry, transcription, and machine control. Some ASR systems are implemented in other systems (and generally referred to as embedded devices) such as appliances and vehicles.
It is known, however, that an ASR system performs more accurately when it is trained on data associated with, or representative of, the application in which it will operate. Various techniques have been proposed to improve the ASR training process, and thus the real-time (test) usage of the ASR system. One technique is generally referred to as feature space transformation where the feature space that is generated by extraction of cepstral features from the input speech signal is transformed in some manner in order to improve the overall operation of the ASR system. One such feature space transformation technique is known as fMPE (described in further detail below) which provides for discriminative training of the feature space for an ASR system using a minimum phone error (MPE) objective function. The result of the fMPE process is a transform parameter space that can be relatively large and, thus, may present a challenge for ASR systems implemented as embedded devices which may have limited processor and memory capacities.